Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations300
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.3 KiB
Average record size in memory120.4 B

Variable types

Numeric14
Categorical1

Alerts

ARM CIRC is highly overall correlated with BMI and 2 other fieldsHigh correlation
BMI is highly overall correlated with ARM CIRC and 2 other fieldsHigh correlation
GENDER (1=M) is highly overall correlated with HEIGHTHigh correlation
HEIGHT is highly overall correlated with GENDER (1=M)High correlation
WAIST is highly overall correlated with ARM CIRC and 2 other fieldsHigh correlation
WEIGHT is highly overall correlated with ARM CIRC and 2 other fieldsHigh correlation

Reproduction

Analysis started2025-04-06 18:02:27.980826
Analysis finished2025-04-06 18:02:37.956599
Duration9.98 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

AGE
Real number (ℝ)

Distinct61
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.04
Minimum18
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:38.180931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q131
median46
Q362
95-th percentile80
Maximum80
Range62
Interquartile range (IQR)31

Descriptive statistics

Standard deviation18.269583
Coefficient of variation (CV)0.38838399
Kurtosis-1.0749315
Mean47.04
Median Absolute Deviation (MAD)15
Skewness0.1168071
Sum14112
Variance333.77766
MonotonicityNot monotonic
2025-04-06T11:02:38.237919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 16
 
5.3%
18 12
 
4.0%
45 10
 
3.3%
46 9
 
3.0%
51 9
 
3.0%
41 9
 
3.0%
60 8
 
2.7%
29 8
 
2.7%
37 7
 
2.3%
57 7
 
2.3%
Other values (51) 205
68.3%
ValueCountFrequency (%)
18 12
4.0%
19 5
1.7%
20 5
1.7%
21 6
2.0%
22 5
1.7%
23 5
1.7%
24 6
2.0%
25 2
 
0.7%
26 4
 
1.3%
27 6
2.0%
ValueCountFrequency (%)
80 16
5.3%
79 1
 
0.3%
78 2
 
0.7%
77 1
 
0.3%
76 3
 
1.0%
75 3
 
1.0%
74 5
 
1.7%
72 2
 
0.7%
70 4
 
1.3%
69 5
 
1.7%

GENDER (1=M)
Categorical

High correlation 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
1
153 
0
147 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 153
51.0%
0 147
49.0%

Length

2025-04-06T11:02:38.300196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-06T11:02:38.336766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 153
51.0%
0 147
49.0%

Most occurring characters

ValueCountFrequency (%)
1 153
51.0%
0 147
49.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 153
51.0%
0 147
49.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 153
51.0%
0 147
49.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 153
51.0%
0 147
49.0%

PULSE
Real number (ℝ)

Distinct32
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.766667
Minimum36
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:38.374861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile54
Q164
median72
Q380
95-th percentile94
Maximum104
Range68
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.128033
Coefficient of variation (CV)0.16899256
Kurtosis-0.019536731
Mean71.766667
Median Absolute Deviation (MAD)8
Skewness0.21621534
Sum21530
Variance147.08919
MonotonicityNot monotonic
2025-04-06T11:02:38.431924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
66 25
 
8.3%
72 25
 
8.3%
64 22
 
7.3%
74 20
 
6.7%
62 19
 
6.3%
82 19
 
6.3%
80 16
 
5.3%
76 13
 
4.3%
78 13
 
4.3%
70 13
 
4.3%
Other values (22) 115
38.3%
ValueCountFrequency (%)
36 1
 
0.3%
40 1
 
0.3%
42 1
 
0.3%
44 1
 
0.3%
50 2
 
0.7%
52 6
2.0%
54 8
2.7%
56 11
3.7%
58 13
4.3%
60 10
3.3%
ValueCountFrequency (%)
104 3
 
1.0%
102 1
 
0.3%
100 2
 
0.7%
98 2
 
0.7%
96 5
1.7%
94 5
1.7%
92 1
 
0.3%
90 5
1.7%
88 5
1.7%
86 9
3.0%

SYSTOLIC
Real number (ℝ)

Distinct39
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.96
Minimum88
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:38.513063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum88
5-th percentile100
Q1112
median121
Q3132
95-th percentile156
Maximum186
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.851687
Coefficient of variation (CV)0.12891743
Kurtosis0.63111323
Mean122.96
Median Absolute Deviation (MAD)9
Skewness0.65817547
Sum36888
Variance251.27599
MonotonicityNot monotonic
2025-04-06T11:02:38.577296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
120 23
 
7.7%
112 21
 
7.0%
122 18
 
6.0%
114 17
 
5.7%
106 16
 
5.3%
126 16
 
5.3%
134 13
 
4.3%
128 12
 
4.0%
130 12
 
4.0%
118 12
 
4.0%
Other values (29) 140
46.7%
ValueCountFrequency (%)
88 1
 
0.3%
92 2
 
0.7%
94 3
 
1.0%
96 3
 
1.0%
98 2
 
0.7%
100 7
2.3%
102 6
 
2.0%
104 6
 
2.0%
106 16
5.3%
108 11
3.7%
ValueCountFrequency (%)
186 1
 
0.3%
168 1
 
0.3%
166 1
 
0.3%
164 1
 
0.3%
162 1
 
0.3%
160 3
1.0%
158 5
1.7%
156 3
1.0%
154 3
1.0%
150 2
 
0.7%

DIASTOLIC
Real number (ℝ)

Distinct31
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.753333
Minimum40
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:38.627387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile52
Q164
median70
Q378
95-th percentile90
Maximum102
Range62
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.616179
Coefficient of variation (CV)0.16417854
Kurtosis0.013769277
Mean70.753333
Median Absolute Deviation (MAD)6
Skewness0.035112753
Sum21226
Variance134.93561
MonotonicityNot monotonic
2025-04-06T11:02:38.685186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
72 27
 
9.0%
68 25
 
8.3%
66 24
 
8.0%
70 22
 
7.3%
64 21
 
7.0%
74 18
 
6.0%
76 17
 
5.7%
78 13
 
4.3%
56 13
 
4.3%
82 12
 
4.0%
Other values (21) 108
36.0%
ValueCountFrequency (%)
40 2
 
0.7%
42 1
 
0.3%
44 1
 
0.3%
46 4
 
1.3%
48 2
 
0.7%
50 4
 
1.3%
52 3
 
1.0%
54 10
3.3%
56 13
4.3%
58 7
2.3%
ValueCountFrequency (%)
102 2
 
0.7%
98 2
 
0.7%
96 2
 
0.7%
94 4
 
1.3%
92 3
 
1.0%
90 6
2.0%
88 12
4.0%
86 2
 
0.7%
84 12
4.0%
82 12
4.0%

HDL
Real number (ℝ)

Distinct65
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.663333
Minimum26
Maximum138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:38.756982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile34
Q143
median52
Q362
95-th percentile84
Maximum138
Range112
Interquartile range (IQR)19

Descriptive statistics

Standard deviation15.438409
Coefficient of variation (CV)0.28769009
Kurtosis3.2539792
Mean53.663333
Median Absolute Deviation (MAD)10
Skewness1.2304132
Sum16099
Variance238.34447
MonotonicityNot monotonic
2025-04-06T11:02:38.909164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 14
 
4.7%
42 13
 
4.3%
43 13
 
4.3%
62 12
 
4.0%
63 11
 
3.7%
52 10
 
3.3%
44 9
 
3.0%
48 9
 
3.0%
49 9
 
3.0%
54 9
 
3.0%
Other values (55) 191
63.7%
ValueCountFrequency (%)
26 3
1.0%
27 1
 
0.3%
29 1
 
0.3%
30 3
1.0%
31 4
1.3%
32 1
 
0.3%
33 1
 
0.3%
34 5
1.7%
35 4
1.3%
36 5
1.7%
ValueCountFrequency (%)
138 1
0.3%
113 1
0.3%
98 1
0.3%
97 1
0.3%
95 1
0.3%
94 1
0.3%
92 1
0.3%
91 1
0.3%
90 1
0.3%
89 1
0.3%

LDL
Real number (ℝ)

Distinct128
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.71333
Minimum39
Maximum251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:38.973548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile63.95
Q185
median113
Q3137.25
95-th percentile173.1
Maximum251
Range212
Interquartile range (IQR)52.25

Descriptive statistics

Standard deviation35.188995
Coefficient of variation (CV)0.30945355
Kurtosis0.22208739
Mean113.71333
Median Absolute Deviation (MAD)26
Skewness0.45852714
Sum34114
Variance1238.2654
MonotonicityNot monotonic
2025-04-06T11:02:39.036924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98 7
 
2.3%
113 7
 
2.3%
85 6
 
2.0%
124 6
 
2.0%
126 6
 
2.0%
118 6
 
2.0%
102 5
 
1.7%
140 5
 
1.7%
112 5
 
1.7%
121 5
 
1.7%
Other values (118) 242
80.7%
ValueCountFrequency (%)
39 1
0.3%
43 1
0.3%
47 1
0.3%
48 1
0.3%
50 2
0.7%
52 2
0.7%
56 1
0.3%
57 1
0.3%
58 1
0.3%
60 1
0.3%
ValueCountFrequency (%)
251 1
0.3%
223 1
0.3%
202 1
0.3%
200 1
0.3%
193 1
0.3%
192 1
0.3%
190 1
0.3%
189 1
0.3%
187 1
0.3%
186 1
0.3%

WHITE
Real number (ℝ)

Distinct82
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5423333
Minimum2.7
Maximum14.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:39.102095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile3.9
Q15.2
median6.2
Q37.825
95-th percentile9.805
Maximum14.3
Range11.6
Interquartile range (IQR)2.625

Descriptive statistics

Standard deviation1.9100092
Coefficient of variation (CV)0.29194617
Kurtosis0.61525028
Mean6.5423333
Median Absolute Deviation (MAD)1.1
Skewness0.7211013
Sum1962.7
Variance3.648135
MonotonicityNot monotonic
2025-04-06T11:02:39.167997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.6 15
 
5.0%
6.1 12
 
4.0%
6.9 11
 
3.7%
5.1 10
 
3.3%
6.4 10
 
3.3%
5.9 9
 
3.0%
5.5 9
 
3.0%
4.6 9
 
3.0%
5.3 8
 
2.7%
5.7 7
 
2.3%
Other values (72) 200
66.7%
ValueCountFrequency (%)
2.7 1
0.3%
3 1
0.3%
3.1 1
0.3%
3.2 1
0.3%
3.3 2
0.7%
3.4 2
0.7%
3.5 2
0.7%
3.7 2
0.7%
3.8 2
0.7%
3.9 2
0.7%
ValueCountFrequency (%)
14.3 1
0.3%
12.6 1
0.3%
12.2 1
0.3%
11.4 2
0.7%
11.3 1
0.3%
11 1
0.3%
10.9 1
0.3%
10.8 1
0.3%
10.4 2
0.7%
10.2 1
0.3%

RED
Real number (ℝ)

Distinct146
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5377667
Minimum3.39
Maximum6.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:39.244458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.39
5-th percentile3.76
Q14.1975
median4.49
Q34.8825
95-th percentile5.32
Maximum6.34
Range2.95
Interquartile range (IQR)0.685

Descriptive statistics

Standard deviation0.48460998
Coefficient of variation (CV)0.10679482
Kurtosis-0.011477971
Mean4.5377667
Median Absolute Deviation (MAD)0.35
Skewness0.26054451
Sum1361.33
Variance0.23484684
MonotonicityNot monotonic
2025-04-06T11:02:39.311164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.32 6
 
2.0%
4.44 5
 
1.7%
4.38 5
 
1.7%
4.82 5
 
1.7%
4.57 5
 
1.7%
4.49 5
 
1.7%
4.35 5
 
1.7%
4.43 5
 
1.7%
4.59 5
 
1.7%
4.61 5
 
1.7%
Other values (136) 249
83.0%
ValueCountFrequency (%)
3.39 1
0.3%
3.45 1
0.3%
3.51 1
0.3%
3.53 1
0.3%
3.54 1
0.3%
3.6 1
0.3%
3.63 1
0.3%
3.64 2
0.7%
3.68 1
0.3%
3.72 2
0.7%
ValueCountFrequency (%)
6.34 1
0.3%
5.8 1
0.3%
5.68 1
0.3%
5.67 1
0.3%
5.6 1
0.3%
5.53 1
0.3%
5.51 1
0.3%
5.47 2
0.7%
5.43 1
0.3%
5.39 1
0.3%

PLATE
Real number (ℝ)

Distinct166
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.37333
Minimum75
Maximum646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:39.364012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile160.95
Q1198
median232
Q3263.5
95-th percentile339.05
Maximum646
Range571
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation64.227496
Coefficient of variation (CV)0.26831517
Kurtosis7.7935708
Mean239.37333
Median Absolute Deviation (MAD)33
Skewness1.7737894
Sum71812
Variance4125.1712
MonotonicityNot monotonic
2025-04-06T11:02:39.421216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
262 6
 
2.0%
206 6
 
2.0%
228 6
 
2.0%
213 5
 
1.7%
240 5
 
1.7%
218 5
 
1.7%
196 5
 
1.7%
203 4
 
1.3%
254 4
 
1.3%
210 4
 
1.3%
Other values (156) 250
83.3%
ValueCountFrequency (%)
75 1
0.3%
103 1
0.3%
110 1
0.3%
131 1
0.3%
132 1
0.3%
136 1
0.3%
138 1
0.3%
140 2
0.7%
144 1
0.3%
149 1
0.3%
ValueCountFrequency (%)
646 1
0.3%
575 1
0.3%
503 1
0.3%
395 2
0.7%
393 1
0.3%
392 1
0.3%
389 1
0.3%
386 1
0.3%
378 1
0.3%
373 1
0.3%

WEIGHT
Real number (ℝ)

High correlation 

Distinct249
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.656333
Minimum39
Maximum150.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:39.494787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile54.07
Q167.075
median80.5
Q392.8
95-th percentile116.33
Maximum150.4
Range111.4
Interquartile range (IQR)25.725

Descriptive statistics

Standard deviation19.675182
Coefficient of variation (CV)0.24095108
Kurtosis0.51569131
Mean81.656333
Median Absolute Deviation (MAD)13.05
Skewness0.62222334
Sum24496.9
Variance387.11277
MonotonicityNot monotonic
2025-04-06T11:02:39.562714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.1 4
 
1.3%
65.3 3
 
1.0%
77.2 3
 
1.0%
79.1 3
 
1.0%
71.7 3
 
1.0%
56.7 3
 
1.0%
86.5 2
 
0.7%
75.9 2
 
0.7%
81.4 2
 
0.7%
102.6 2
 
0.7%
Other values (239) 273
91.0%
ValueCountFrequency (%)
39 1
0.3%
43.3 1
0.3%
45.2 1
0.3%
46.3 1
0.3%
46.9 1
0.3%
48 1
0.3%
48.3 1
0.3%
48.5 1
0.3%
50.3 1
0.3%
50.4 2
0.7%
ValueCountFrequency (%)
150.4 1
0.3%
144.9 1
0.3%
140.1 1
0.3%
138.9 1
0.3%
133.3 1
0.3%
132.1 1
0.3%
127.8 1
0.3%
127.5 1
0.3%
126.4 1
0.3%
126 1
0.3%

HEIGHT
Real number (ℝ)

High correlation 

Distinct204
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.03033
Minimum134.5
Maximum193.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:39.625875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum134.5
5-th percentile153.3
Q1161.575
median168.35
Q3174.625
95-th percentile183.12
Maximum193.3
Range58.8
Interquartile range (IQR)13.05

Descriptive statistics

Standard deviation9.5793385
Coefficient of variation (CV)0.057009578
Kurtosis-0.11904438
Mean168.03033
Median Absolute Deviation (MAD)6.5
Skewness-0.1049762
Sum50409.1
Variance91.763726
MonotonicityNot monotonic
2025-04-06T11:02:39.689881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.1 5
 
1.7%
155.6 4
 
1.3%
174.4 4
 
1.3%
178.5 3
 
1.0%
170 3
 
1.0%
170.9 3
 
1.0%
168.5 3
 
1.0%
155 3
 
1.0%
181.1 3
 
1.0%
158.4 3
 
1.0%
Other values (194) 266
88.7%
ValueCountFrequency (%)
134.5 1
0.3%
144.2 1
0.3%
144.4 1
0.3%
146.7 1
0.3%
147.2 1
0.3%
147.9 1
0.3%
148.7 1
0.3%
149 1
0.3%
149.5 1
0.3%
150.6 1
0.3%
ValueCountFrequency (%)
193.3 1
0.3%
190.3 1
0.3%
189.7 1
0.3%
188.4 1
0.3%
187.2 1
0.3%
186.3 1
0.3%
186 1
0.3%
185.7 1
0.3%
185.1 2
0.7%
184.3 1
0.3%

WAIST
Real number (ℝ)

High correlation 

Distinct224
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.178333
Minimum64.4
Maximum170.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:39.755209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum64.4
5-th percentile76.45
Q187.875
median96.95
Q3109.1
95-th percentile128.35
Maximum170.5
Range106.1
Interquartile range (IQR)21.225

Descriptive statistics

Standard deviation16.477337
Coefficient of variation (CV)0.16613848
Kurtosis1.0256019
Mean99.178333
Median Absolute Deviation (MAD)10.35
Skewness0.72242116
Sum29753.5
Variance271.50264
MonotonicityNot monotonic
2025-04-06T11:02:39.830154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 5
 
1.7%
115 4
 
1.3%
92.6 3
 
1.0%
78 3
 
1.0%
90.5 3
 
1.0%
88.2 3
 
1.0%
78.8 3
 
1.0%
100.8 3
 
1.0%
81.9 3
 
1.0%
111.6 3
 
1.0%
Other values (214) 267
89.0%
ValueCountFrequency (%)
64.4 1
 
0.3%
65.8 1
 
0.3%
67.4 1
 
0.3%
68.8 2
0.7%
70.2 1
 
0.3%
71.5 1
 
0.3%
72.1 1
 
0.3%
74 2
0.7%
74.5 3
1.0%
75.5 2
0.7%
ValueCountFrequency (%)
170.5 1
0.3%
148.1 1
0.3%
144.4 1
0.3%
144.2 1
0.3%
144 1
0.3%
142.6 1
0.3%
140.8 1
0.3%
140.6 1
0.3%
138.1 1
0.3%
133.5 1
0.3%

ARM CIRC
Real number (ℝ)

High correlation 

Distinct145
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.078333
Minimum20.5
Maximum46.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:39.890789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.5
5-th percentile25.7
Q129.475
median33.05
Q336.325
95-th percentile40.705
Maximum46.6
Range26.1
Interquartile range (IQR)6.85

Descriptive statistics

Standard deviation4.8150368
Coefficient of variation (CV)0.14556467
Kurtosis-0.30178492
Mean33.078333
Median Absolute Deviation (MAD)3.5
Skewness0.20701806
Sum9923.5
Variance23.184579
MonotonicityNot monotonic
2025-04-06T11:02:39.961216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 9
 
3.0%
35.3 6
 
2.0%
34.7 6
 
2.0%
33.5 6
 
2.0%
32.2 6
 
2.0%
34.2 5
 
1.7%
37 5
 
1.7%
35 5
 
1.7%
27 4
 
1.3%
34.5 4
 
1.3%
Other values (135) 244
81.3%
ValueCountFrequency (%)
20.5 1
 
0.3%
22.2 1
 
0.3%
23.6 1
 
0.3%
23.7 1
 
0.3%
24 1
 
0.3%
24.4 1
 
0.3%
24.5 2
0.7%
24.7 1
 
0.3%
25.2 2
0.7%
25.5 3
1.0%
ValueCountFrequency (%)
46.6 1
0.3%
46.3 1
0.3%
45.6 1
0.3%
45 1
0.3%
44.4 1
0.3%
44.3 1
0.3%
44.1 1
0.3%
42 1
0.3%
41.9 1
0.3%
41.6 1
0.3%

BMI
Real number (ℝ)

High correlation 

Distinct169
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.912333
Minimum15.9
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-04-06T11:02:40.095982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.9
5-th percentile19.995
Q124.5
median28
Q331.975
95-th percentile41.205
Maximum59
Range43.1
Interquartile range (IQR)7.475

Descriptive statistics

Standard deviation6.7467035
Coefficient of variation (CV)0.23335036
Kurtosis2.2587085
Mean28.912333
Median Absolute Deviation (MAD)3.8
Skewness1.1595404
Sum8673.7
Variance45.518008
MonotonicityNot monotonic
2025-04-06T11:02:40.159739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.5 5
 
1.7%
25.2 5
 
1.7%
25.8 4
 
1.3%
26.1 4
 
1.3%
31.1 4
 
1.3%
24.8 4
 
1.3%
21.6 4
 
1.3%
29.4 4
 
1.3%
33.3 4
 
1.3%
23.3 4
 
1.3%
Other values (159) 258
86.0%
ValueCountFrequency (%)
15.9 1
0.3%
17 1
0.3%
17.9 1
0.3%
18.1 1
0.3%
18.7 2
0.7%
18.9 1
0.3%
19 1
0.3%
19.2 1
0.3%
19.3 1
0.3%
19.4 1
0.3%
ValueCountFrequency (%)
59 1
0.3%
56.8 1
0.3%
52.6 1
0.3%
48 1
0.3%
47.2 1
0.3%
47 1
0.3%
45.9 2
0.7%
45.5 1
0.3%
45.4 1
0.3%
45 1
0.3%

Interactions

2025-04-06T11:02:37.079612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T11:02:28.376659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-06T11:02:33.101404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-06T11:02:32.268439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T11:02:32.879240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T11:02:33.562594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T11:02:34.210635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T11:02:34.828763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T11:02:35.527519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T11:02:36.154058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-06T11:02:28.837857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-06T11:02:33.614845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-06T11:02:35.708088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T11:02:36.326545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T11:02:36.960143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-06T11:02:40.214710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AGEARM CIRCBMIDIASTOLICGENDER (1=M)HDLHEIGHTLDLPLATEPULSEREDSYSTOLICWAISTWEIGHTWHITE
AGE1.0000.0040.1150.1040.0240.078-0.1230.121-0.097-0.175-0.2230.4840.2610.045-0.093
ARM CIRC0.0041.0000.8810.2690.156-0.3180.1930.094-0.003-0.0240.1730.1910.7940.9000.124
BMI0.1150.8811.0000.2710.164-0.279-0.0460.1090.0670.0640.0410.2610.9060.8830.158
DIASTOLIC0.1040.2690.2711.0000.000-0.0600.0450.1750.0580.1110.1550.3510.2590.271-0.018
GENDER (1=M)0.0240.1560.1640.0001.0000.3170.6510.0000.1820.1260.4190.1040.0700.2440.000
HDL0.078-0.318-0.279-0.0600.3171.000-0.2480.0760.038-0.023-0.269-0.020-0.350-0.367-0.198
HEIGHT-0.1230.193-0.0460.0450.651-0.2481.0000.041-0.186-0.0790.386-0.0280.1170.3890.032
LDL0.1210.0940.1090.1750.0000.0760.0411.0000.131-0.0180.2650.1220.0920.104-0.073
PLATE-0.097-0.0030.0670.0580.1820.038-0.1860.1311.0000.178-0.002-0.0070.014-0.0360.296
PULSE-0.175-0.0240.0640.1110.126-0.023-0.079-0.0180.1781.000-0.004-0.1520.0530.0260.168
RED-0.2230.1730.0410.1550.419-0.2690.3860.265-0.002-0.0041.000-0.0410.0800.1970.058
SYSTOLIC0.4840.1910.2610.3510.104-0.020-0.0280.122-0.007-0.152-0.0411.0000.2930.2270.079
WAIST0.2610.7940.9060.2590.070-0.3500.1170.0920.0140.0530.0800.2931.0000.8850.217
WEIGHT0.0450.9000.8830.2710.244-0.3670.3890.104-0.0360.0260.1970.2270.8851.0000.169
WHITE-0.0930.1240.158-0.0180.000-0.1980.032-0.0730.2960.1680.0580.0790.2170.1691.000

Missing values

2025-04-06T11:02:37.730809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-06T11:02:37.793684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AGEGENDER (1=M)PULSESYSTOLICDIASTOLICHDLLDLWHITEREDPLATEWEIGHTHEIGHTWAISTARM CIRCBMI
0430801007073688.74.8031998.6172.0120.440.733.3
15718411270351164.94.7318796.9186.0107.837.028.0
23809413494362236.94.47297108.2154.4120.344.345.4
3801741266437837.54.3217073.1160.597.230.328.4
43415011468501046.14.9514083.1179.095.134.025.9
5771601346055755.73.9519286.5166.7112.031.431.1
62915211864531284.14.6819164.1178.578.027.420.1
76905813880401408.14.6028679.2155.7103.534.232.7
84406611466451368.04.0926364.2157.689.732.525.8
93516212470621105.65.47193118.8180.4112.040.036.5
AGEGENDER (1=M)PULSESYSTOLICDIASTOLICHDLLDLWHITEREDPLATEWEIGHTHEIGHTWAISTARM CIRCBMI
2901817210644401244.05.1722171.6172.878.131.024.0
2914906216496771265.54.9819078.4163.490.033.029.4
292671621368239627.73.90305110.2169.1125.539.038.5
293180821007252707.24.0723671.4162.586.234.227.0
294420761306462608.04.0013262.0175.674.526.120.1
295241949662431027.05.2926056.3162.778.427.921.3
296500941328442697.94.35244103.2146.7142.639.548.0
2975318613274421128.44.0775102.6181.0117.736.531.3
2983407410454441037.64.3629296.1162.2109.037.036.5
2993119010470641126.05.0719756.4165.474.026.520.6